The Ames Housing dataset
contains various features about houses in Ames, Iowa, and their sale
prices. The goal of this analysis is to understand the key factors that
influence house prices in this area.
1. Explore the dataset and identify important features.
2.
Analyze the correlation between different features and sale price.
3. Provide insights and visualizations to help understand the dataset
better.
1. Positive Correlation:
• There is a clear positive
correlation between the overall quality of houses and their sale prices.
As the quality rating increases from “Very Poor” to “Very Excellent,”
the median sale price also increases.
2. Median Sale Prices:
• Houses with lower quality ratings (e.g., “Very Poor,” “Poor”) have
lower median sale prices.
• Houses with higher quality ratings
(e.g., “Very Good,” “Excellent,” “Very Excellent”) have significantly
higher median sale prices.
3. Variation in Sale Prices:
•
The interquartile range (IQR), which is represented by the height of
each box, indicates the variability in sale prices within each quality
category. Higher quality ratings tend to have a wider range of sale
prices, suggesting more variability.
Further analysis could explore the specific features that
contribute to the higher prices of large houses (e.g., number of
bedrooms, location, year built).
1.Neighborhood Influence:
- Different neighborhoods
have different reaction in how SF impacts the sale price.
- Higher
sale prices are observed in certain neighborhoods, indicating location
as a significant factor.
2.Positive Correlation Across
Neighborhoods:
- There is a general positive correlation between
living area and sale price across all neighborhoods.Larger houses tend
to sell for higher prices in most neighborhoods.
3.Inconsistency in Price Trends:
Some neighborhoods show a steeper
increase in sale price with an increase in living area, highlighting the
premium placed on larger homes in those areas.
- The inconsistency
suggests that additional factors beyond living area and neighborhood
influence house prices.
1. Cluster Characteristics:
- Cluster 1 (Red):
Represents homes with moderate living areas and sale prices. These
houses might be in average neighborhoods, offering a balance between
size and affordability.
- Cluster 2 (Green): Likely consists of
smaller homes with lower overall quality and fewer total rooms, leading
to lower sale prices. These houses might be in less desirable
neighborhoods or older areas.
- Cluster 3 (Blue): Comprises larger
homes with higher sale prices, indicating premium features such as newer
construction, higher quality, and more desirable neighborhoods.
2. Influence of Neighborhood and Features:
The clustering model considers multiple variables such as SF,
Overall_Qual, Year_Built, TotRms_AbvGrd, and Neighborhood. Higher
quality and newer homes in desirable neighborhoods tend to cluster
together, while older, smaller, and lower-quality homes form separate
clusters.
3. Market Segmentation:
The identified clusters reflect different segments of the housing
market.
- Cluster 1 represents entry-level housing
- Cluster 2
represents mid-range homes
- Cluster 3 represents high-end
properties.
This segmentation helps in understanding the
distribution and pricing strategies within the housing market in Ames,
Iowa.